A Multi-Scale Traffic Object Detection Algorithm for Road Scenes Based on Improved YOLOv5

Li, Ang; Sun, Shijie; Zhang, Zhaoyang; Feng, Mingtao; Wu, Chengzhong; Li, Wang

doi:10.3390/electronics12040878

Cited by 30 publications

(7 citation statements)

References 40 publications

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“…Li et al [11] present an enhanced multiscale YOLOv5s algorithm, and CARAFE element (a content-aware reassembly of feature) was presented in the feature fusion part to boost the feature fusion. Rather than the original convolution infrastructure, an SPD-Conv CNN Element was devised to enrich the computational efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Leveraging Marine Predators Algorithm with Deep Learning Object Detection for Accurate and Efficient Detection of Pedestrians

Gogineni

Bhuyan

Lydia³

2023

Preprint

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Pedestrian detection using object detection and deep learning has been found to be effective method for identifying pedestrians in video frames or images accurately. It is more commonly used in many real-time applications, such as security observing systems, autonomous driving systems, and robotics. The combination of deep learning techniques and object detection algorithms allows efficient and robust detection of pedestrians in several real-time scenarios. But it is necessary to improve the detection efficacy for complex environments such as cases with worse visibility due to weather or daytime, crowd scenes, and rare pose samples. Continuous improvement and research in DL algorithms, dataset collection, and training models contribute to accelerating the robustness and accuracy of pedestrian detection systems. Therefore, this study designs a new marine predator algorithm with deep learning-based pedestrian detection and classification (MPADLB-PDC) technique. The objective of the MPADLB-PDC approach lies in the accurate recognition and classification of pedestrians. To achieve this, the MPADLB-PDC technique involves two major processes, namely object detection and classification. In the first stage, the MPADLBPDC technique uses an improved YOLOv7 object detector for the recognition of the objects in the frame. Next, in the second stage, the ensemble classifier comprises three classifiers such as deep feed-forward neural network (DFFNN), extreme learning machine (ELM), and long short-term memory (LSTM). To improve the recognition performance of the ensemble classifier, the MPA is used to optimally select the parameters related to it. The simulation outcome of the MPADLB-PDC system was validated on the pedestrian database, and the outcome can be studied interms of various aspects. The simulation values validated the better outcome of the MPADLB-PDC system compared to other approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Leveraging Marine Predators Algorithm with Deep Learning Object Detection for Accurate and Efficient Detection of Pedestrians

Gogineni

Bhuyan

Lydia³

2023

Preprint

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“…When integrated into the feature extraction module, this mechanism enhances the model's detection performance. It enables the extraction of fundamental location details from relevant defects, aiding tasks like identifying localized insulators 42 or traffic objects 43 . The introduction of this attention mechanism aims to enhance the network's ability to extract and represent image features, making it more expressive without increasing its depth.Figure 5 shows its channel attention structure.…”

Section: Normalization-based Attention Modulementioning

confidence: 99%

Steel Surface Defect Detection Based on Multi-Layer Fusion Networks

Li,

Liu,

Yin

et al. 2023

Preprint

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Detecting defects on steel surfaces is crucial for ensuring product quality and production safety in industrial settings. Object detection using deep learning, particularly the YOLOv5 model, has become a widely adopted method for this purpose. However, the complex shapes of current steel surface defects pose challenges for precise detection, especially when using low-cost recognition devices with small resolution images. To address these challenges, we integrated the RepBi-PAN fusion network into YOLOv5, enhancing the detection capability for large targets in complex backgrounds. To mitigate issues related to the premature introduction of shallow features and decrease in Precision, we optimized the model structure by incorporating the DenseNet structure into the backbone for improved feature extraction. Additionally, we introduced the Normalized Attention Module (NAM) to enhance the detection capability for small targets. Experimental results demonstrate the effectiveness of the enhanced model, showing a 4.1% increase in mean average precision (mAP), a 3.2% improvement in precision, and a 2.4% enhancement in recall. The improved algorithm outperforms the original in complex backgrounds and recognizing small targets, addressing limitations of the Rep-Bi network. Compared to other YOLO algorithms, our approach achieves optimal values for recall and mAP while maintaining a smaller model size. In comparison with YOLOv8, the improved model surpasses all V8 models, being only 0.5% below the precision of the largest YOLOv8x model. Simultaneously, the improved model is smaller and has fewer parameters compared to all models in the YOLOv8 series, with slightly higher GFLOPs than the smaller v8 models.

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“…In recent years, with the rapid development of deep learning technology, many detection methods based on deep learning have been widely used in various scenarios, including materials [6], medical [7], agriculture [8], transportation [9], textile [10] and other fields. Especially in the industrial sector, deep learning-based surface defect detection has become a popular application.…”

Section: Introductionmentioning

confidence: 99%

Crack identification method for magnetic particle inspection of bearing rings based on improved Yolov5

Yang,

Zuo,

et al. 2024

Meas. Sci. Technol.

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The fluorescent magnetic particle inspection technique is often used for surface crack detection of bearing rings due to its advantages of simple operation and high sensitivity. With the development of computer vision technology, more and more visual algorithms are used in magnetic particle inspection for defect detection. However, most of these current algorithm models have low detection accuracy and poor efficiency, making it difficult to meet the precision requirements of production testing and affecting the overall pace of production processes. To address this problem, this paper proposes an improved algorithm model based on Yolov5. Firstly, MobileNetV3-small is utilized to construct the backbone feature extraction network, reducing the network's parameter count and enhancing its detection speed. In addition, BiFPN is implemented to facilitate swift and efficient multi-scale feature fusion, while the C3 module in the neck is replaced with C2f to enhance detection precision. Finally, Focal-Loss EIoU is adopted as the loss function to improve the model's accuracy in positioning the crack borders. Experimental results demonstrate that the precision of this model in detecting surface cracks in bearing rings achieves an impressive 95.1%, while the recall reaches 90.4%. The mAP stands at 0.946. When compared to the original Yolov5s network, this model showcases a reduction in network parameters by 32.1% and a significant increase in FPS by 40.0%. These improvements effectively fulfill the production process's demands for crack detection tasks, providing a balance between accuracy and efficiency.

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A Multi-Scale Traffic Object Detection Algorithm for Road Scenes Based on Improved YOLOv5

Cited by 30 publications

References 40 publications

Leveraging Marine Predators Algorithm with Deep Learning Object Detection for Accurate and Efficient Detection of Pedestrians

Leveraging Marine Predators Algorithm with Deep Learning Object Detection for Accurate and Efficient Detection of Pedestrians

Steel Surface Defect Detection Based on Multi-Layer Fusion Networks

Crack identification method for magnetic particle inspection of bearing rings based on improved Yolov5

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